Azure Data Factory vs Splunk

Overview

Splunk

Stacks772

Followers1.0K

Votes20

Azure Data Factory

Stacks253

Followers484

Votes0

GitHub Stars516

Forks610

Azure Data Factory vs Splunk: What are the differences?

Introduction: Azure Data Factory and Splunk are two popular tools used for managing and analyzing data. While both aim to help businesses make sense of their data, there are key differences between the two platforms.

Data Source Support: Azure Data Factory is a cloud-based data integration service developed by Microsoft, which allows users to create data-driven workflows. It supports a wide variety of data sources, including on-premises databases, cloud storage solutions, and even social media platforms. On the other hand, Splunk is primarily a log analysis platform that specializes in handling machine-generated data such as logs, metrics, and events.
Data Processing Capabilities: Azure Data Factory provides a broad range of data transformation and manipulation capabilities. It allows users to transform data using mapping, filtering, aggregating, and joining operations. Additionally, it supports data extraction, transformation, and loading (ETL) workflows. Conversely, Splunk focuses more on indexing and searching data rather than transforming it. It excels in real-time monitoring, search, and analysis of large amounts of data coming from various sources.
Scalability and Performance: Azure Data Factory offers scalable data integration and processing capabilities, leveraging the power of Microsoft Azure's infrastructure. It can handle large volumes of data and parallelize workloads efficiently. Splunk, on the other hand, is designed to scale horizontally by adding more indexers and search heads. It provides high-performance search and indexing capabilities, even for massive datasets.
Data Visualization and Dashboards: Azure Data Factory provides basic visualization capabilities through integration with Power BI, a business analytics tool. Users can create interactive reports and dashboards to gain insights from their data. In contrast, Splunk offers rich and powerful visualization features right out of the box. It provides customizable dashboards, charts, and graphs to visualize data and share insights with stakeholders.
Pricing Model: Azure Data Factory follows a consumption-based pricing model, where users pay for the resources they utilize. The cost depends on the number and type of activities executed. On the other hand, Splunk employs a data ingestion-based pricing model. Users have to purchase a license based on the amount of data ingested into Splunk per day. The cost increases as the volume of data processed and stored goes up.
Ecosystem and Integrations: Azure Data Factory seamlessly integrates with other Azure services, such as Azure Blob Storage, Azure SQL Database, and Azure Databricks. It also supports third-party integrations for data sources and destinations. In contrast, Splunk has a vast ecosystem of apps and add-ons developed by Splunk and its community. These extensions enable Splunk to integrate with a wide range of technologies and extend its functionality.

In Summary, Azure Data Factory provides robust data integration and transformation capabilities with a focus on scalability and integration with the Microsoft Azure ecosystem. Splunk, on the other hand, excels in real-time log analysis and visualization, offering powerful search and indexing features.

Share your Stack

Help developers discover the tools you use. Get visibility for your team's tech choices and contribute to the community's knowledge.

View Docs

CLI (Node.js)

Manual

Advice on Splunk, Azure Data Factory

Vamshi

Data Engineer at Tata Consultancy Services

May 29, 2020

Needs adviceon

PySpark

Azure Data Factory

Databricks

I have to collect different data from multiple sources and store them in a single cloud location. Then perform cleaning and transforming using PySpark, and push the end results to other applications like reporting tools, etc. What would be the best solution? I can only think of Azure Data Factory + Databricks. Are there any alternatives to #AWS services + Databricks?

269k views269k

Comments

Detailed Comparison

Splunk	Azure Data Factory
It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.	It is a service designed to allow developers to integrate disparate data sources. It is a platform somewhat like SSIS in the cloud to manage the data you have both on-prem and in the cloud.
Predict and prevent problems with one unified monitoring experience; Streamline your entire security stack with Splunk as the nerve center; Detect, investigate and diagnose problems easily with end-to-end observability	Real-Time Integration; Parallel Processing; Data Chunker; Data Masking; Proactive Monitoring; Big Data Processing
Statistics
GitHub Stars -	GitHub Stars 516
GitHub Forks -	GitHub Forks 610
Stacks 772	Stacks 253
Followers 1.0K	Followers 484
Votes 20	Votes 0
Pros & Cons
Pros 3 Alert system based on custom query results 3 API for searching logs, running reports 2 Ability to style search results into reports 2 Query engine supports joining, aggregation, stats, etc 2 Dashboarding on any log contents Cons 1 Splunk query language rich so lots to learn	No community feedback yet
Integrations
No integrations available	Octotree Java .NET

What are some alternatives to Splunk, Azure Data Factory?

Papertrail

Papertrail helps detect, resolve, and avoid infrastructure problems using log messages. Papertrail's practicality comes from our own experience as sysadmins, developers, and entrepreneurs.

Logmatic

Get a clear overview of what is happening across your distributed environments, and spot the needle in the haystack in no time. Build dynamic analyses and identify improvements for your software, your user experience and your business.

Loggly

It is a SaaS solution to manage your log data. There is nothing to install and updates are automatically applied to your Loggly subdomain.

Apache Spark

Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.

Logentries

Logentries makes machine-generated log data easily accessible to IT operations, development, and business analysis teams of all sizes. With the broadest platform support and an open API, Logentries brings the value of log-level data to any system, to any team member, and to a community of more than 25,000 worldwide users.

Logstash

Logstash is a tool for managing events and logs. You can use it to collect logs, parse them, and store them for later use (like, for searching). If you store them in Elasticsearch, you can view and analyze them with Kibana.

Graylog

Centralize and aggregate all your log files for 100% visibility. Use our powerful query language to search through terabytes of log data to discover and analyze important information.

Presto

Distributed SQL Query Engine for Big Data

Amazon Athena

Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.

Sematext

Sematext pulls together performance monitoring, logs, user experience and synthetic monitoring that tools organizations need to troubleshoot performance issues faster.

Related Comparisons

Azure Data Factory vs Splunk: What are the differences?

Data Source Support: Azure Data Factory is a cloud-based data integration service developed by Microsoft, which allows users to create data-driven workflows. It supports a wide variety of data sources, including on-premises databases, cloud storage solutions, and even social media platforms. On the other hand, Splunk is primarily a log analysis platform that specializes in handling machine-generated data such as logs, metrics, and events.
Data Processing Capabilities: Azure Data Factory provides a broad range of data transformation and manipulation capabilities. It allows users to transform data using mapping, filtering, aggregating, and joining operations. Additionally, it supports data extraction, transformation, and loading (ETL) workflows. Conversely, Splunk focuses more on indexing and searching data rather than transforming it. It excels in real-time monitoring, search, and analysis of large amounts of data coming from various sources.
Scalability and Performance: Azure Data Factory offers scalable data integration and processing capabilities, leveraging the power of Microsoft Azure's infrastructure. It can handle large volumes of data and parallelize workloads efficiently. Splunk, on the other hand, is designed to scale horizontally by adding more indexers and search heads. It provides high-performance search and indexing capabilities, even for massive datasets.
Data Visualization and Dashboards: Azure Data Factory provides basic visualization capabilities through integration with Power BI, a business analytics tool. Users can create interactive reports and dashboards to gain insights from their data. In contrast, Splunk offers rich and powerful visualization features right out of the box. It provides customizable dashboards, charts, and graphs to visualize data and share insights with stakeholders.
Pricing Model: Azure Data Factory follows a consumption-based pricing model, where users pay for the resources they utilize. The cost depends on the number and type of activities executed. On the other hand, Splunk employs a data ingestion-based pricing model. Users have to purchase a license based on the amount of data ingested into Splunk per day. The cost increases as the volume of data processed and stored goes up.
Ecosystem and Integrations: Azure Data Factory seamlessly integrates with other Azure services, such as Azure Blob Storage, Azure SQL Database, and Azure Databricks. It also supports third-party integrations for data sources and destinations. In contrast, Splunk has a vast ecosystem of apps and add-ons developed by Splunk and its community. These extensions enable Splunk to integrate with a wide range of technologies and extend its functionality.

Azure Data Factory vs Splunk

Overview